With the development of information and intelligence technology, there is more and more research on intelligent recognizing of the sport of tennis. Existing technology for tracking and recognizing the swing movement of tennis racket mainly includes a recognizing technology based on image video and recognizing technology based on sensors.
The recognizing technology based on image video mainly uses a high-speed camera to capture a mark provided on the tennis ball, thereby obtaining movement information of the tennis racket, and this method depends on analyzing and processing the image and the video. The recognizing technology based on sensors mainly uses an accelerometer sensor, a gyroscope sensor or the like to measure parameter information such as the special moving path, the speed and the rotational angle of various types of tennis, and further analyzing the parameters to recognize the racket swing.
However, High-speed cameras are necessary in recognizing technology based on image video, and the high-speed camera is expensive and inconvenient to carry. Accordingly, the recognizing technology based on image video only applies to professional training scenarios, and has high requirements on hardware platform and processing capability, which limits its scope of application.
On the other hand, although there are little limitations on the cost and application of the recognizing technology based on sensors, it is difficult to extract characteristic values of various types of movements due to the fact that the information such as accelerated speed and rotational angle or the like, which serve as the characteristic value, among various racket swing movements are similar, resulting in relatively high complexity and low distinguishability, and in turn resulting in too many errors in recognition result of the movement of tennis racket.